Download or read book Machine Learning in Non stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with non-stationarity is one of modem machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.

Download or read book Continual Machine Learning for Non stationary Data Analysis written by Honglin Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Non stationary Environments written by Yi He and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning in Non Stationary Environments written by Motoaki Kawanabe and published by . This book was released on with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

Download or read book Machine Learning Refined written by Jeremy Watt and published by Cambridge University Press. This book was released on 2016-09-08 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new, intuitive approach to machine learning, covering fundamental concepts and real-world applications, with practical MATLAB-based exercises.

Download or read book Adapting Machine Learning to Non stationary Environments written by Wintheiser Donnie and published by . This book was released on 2023-04-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning stimulates a broad range of computational methods that exploit experience, which typically takes the form of electronic data, to make profitable decisions or accurate predictions. To date, the machine learning models have been applied to extensive application domains across diverse fields, including but not limited to computer vision [1, 2, 3], natural language processing [4, 5, 6], robotic control [7, 8], and cyber security [9, 10, 11].

Download or read book Lifelong Machine Learning written by Zhiyuan Chen (Computer scientist) and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks--which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning--most notably, multi-task learning, transfer learning, and metalearning--because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Download or read book Data Analysis Machine Learning and Applications written by Christine Preisach and published by . This book was released on 2008-07-17 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Special Issue on continuous Optimization and Applications in Machine Learning and Data Analytics written by Miguel F. Anjos and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Rethinking Continual Learning Approach and Study Out of distribution Generalization Algorithms written by Touraj Laleh and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the challenges of current machine learning systems is that standard AI paradigms are not good at transferring (or leveraging) knowledge across tasks. While many systems have been trained and achieved high performance on a specific distribution of a task, it is not easy to train AI systems that can perform well on a diverse set of tasks that belong to different distributions. This problem has been addressed from different perspectives in different domains including continual learning and out-of-distribution generalization. If an AI system is trained on a set of tasks belonging to different distributions, it could forget the knowledge it acquired from previous tasks. In continual learning, this process results in catastrophic forgetting which is one of the core issues of this domain. The first research project in this thesis focuses on the comparison of a chaotic learner and a naive continual learning setup. Training a deep neural network model usually requires multiple iterations, or epochs, over the training data set, to better estimate the parameters of the model. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to the model. In this chapter, we propose a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not update network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setups. Interestingly, our experiments on different datasets show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning. Deep Learning models have a naive out-of-distribution~(OoD) generalization performance where the testing distribution is unknown and different from the training. In the last years, there have been many research projects to compare OoD algorithms, including average and score-based methods. However, most proposed methods do not consider the level of difficulty of tasks. The second research project in this thesis, analysis some logical and practical strengths and drawbacks of existing methods for comparing and ranking OoD algorithms. We propose a novel ranking approach to define the task difficulty ratios to compare OoD generalization algorithms. We compared the average, score-based, and difficulty-based rankings of four selected tasks from the WILDS benchmark and five popular OoD algorithms for the experiment. The analysis shows significant changes in the ranking orders compared with current ranking approaches.

Download or read book A Comparison of Machine Learning Model Validation Schemes for Non stationary Time Series Data written by Matthias Schnaubelt and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample performance in both model selection and assessment. For time series data, however, it is unclear whether forwardvalidation schemes, i.e., schemes that keep the temporal order of observations, should be preferred. In this paper, we perform a comprehensive empirical study of eight common validation schemes. We introduce a study design that perturbs global stationarity by introducing a slow evolution of the underlying data-generating process. Our results demonstrate that, even for relatively small perturbations, commonly used cross-validation schemes often yield estimates with the largest bias and variance, and forward-validation schemes yield better estimates of the out-of-sample error. We provide an interpretation of these results in terms of an additional evolution-induced bias and the sample-size dependent estimation error. Using a large-scale financial data set, we demonstrate the practical significance in a replication study of a statistical arbitrage problem. We conclude with some general guidelines on the selection of suitable validation schemes for time series data.

Download or read book TKINTER DATA SCIENCE AND MACHINE LEARNING written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-09-02 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project, we embarked on a comprehensive journey through the world of machine learning and model evaluation. Our primary goal was to develop a Tkinter GUI and assess various machine learning models on a given dataset to identify the best-performing one. This process is essential in solving real-world problems, as it helps us select the most suitable algorithm for a specific task. By crafting this Tkinter-powered GUI, we provided an accessible and user-friendly interface for users engaging with machine learning models. It simplified intricate processes, allowing users to load data, select models, initiate training, and visualize results without necessitating code expertise or command-line operations. This GUI introduced a higher degree of usability and accessibility to the machine learning workflow, accommodating users with diverse levels of technical proficiency. We began by loading and preprocessing the dataset, a fundamental step in any machine learning project. Proper data preprocessing involves tasks such as handling missing values, encoding categorical features, and scaling numerical attributes. These operations ensure that the data is in a format suitable for training and testing machine learning models. Once our data was ready, we moved on to the model selection phase. We evaluated multiple machine learning algorithms, each with its strengths and weaknesses. The models we explored included Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Decision Trees, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). For each model, we employed a systematic approach to find the best hyperparameters using grid search with cross-validation. This technique allowed us to explore different combinations of hyperparameters and select the configuration that yielded the highest accuracy on the training data. These hyperparameters included settings like the number of estimators, learning rate, and kernel function, depending on the specific model. After obtaining the best hyperparameters for each model, we trained them on our preprocessed dataset. This training process involved using the training data to teach the model to make predictions on new, unseen examples. Once trained, the models were ready for evaluation. We assessed the performance of each model using a set of well-established evaluation metrics. These metrics included accuracy, precision, recall, and F1-score. Accuracy measured the overall correctness of predictions, while precision quantified the proportion of true positive predictions out of all positive predictions. Recall, on the other hand, represented the proportion of true positive predictions out of all actual positives, highlighting a model's ability to identify positive cases. The F1-score combined precision and recall into a single metric, helping us gauge the overall balance between these two aspects. To visualize the model's performance, we created key graphical representations. These included confusion matrices, which showed the number of true positive, true negative, false positive, and false negative predictions, aiding in understanding the model's classification results. Additionally, we generated Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) scores, which depicted a model's ability to distinguish between classes. High AUC values indicated excellent model performance. Furthermore, we constructed true values versus predicted values diagrams to provide insights into how well our models aligned with the actual data distribution. Learning curves were also generated to observe a model's performance as a function of training data size, helping us assess whether the model was overfitting or underfitting. Lastly, we presented the results in a clear and organized manner, saving them to Excel files for easy reference. This allowed us to compare the performance of different models and make an informed choice about which one to select for our specific task. In summary, this project was a comprehensive exploration of the machine learning model development and evaluation process. We prepared the data, selected and fine-tuned various models, assessed their performance using multiple metrics and visualizations, and ultimately arrived at a well-informed decision about the most suitable model for our dataset. This approach serves as a valuable blueprint for tackling real-world machine learning challenges effectively.

Download or read book DATA VISUALIZATION TIME SERIES FORECASTING AND PREDICTION USING MACHINE LEARNING WITH TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-09-06 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project is a comprehensive and multifaceted application that leverages data visualization, time-series forecasting, and machine learning techniques to gain insights into bitcoin data and make predictions. This project serves as a valuable tool for financial analysts, traders, and investors seeking to make informed decisions in the stock market. The project begins with data visualization, where historical bitcoin market data is visually represented using various plots and charts. This provides users with an intuitive understanding of the data's trends, patterns, and fluctuations. Features distribution analysis is conducted to assess the statistical properties of the dataset, helping users identify key characteristics that may impact forecasting and prediction. One of the project's core functionalities is time-series forecasting. Through a user-friendly interface built with Tkinter, users can select a stock symbol and specify the time horizon for forecasting. The project supports multiple machine learning regressors, such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, Lasso, Ridge, AdaBoost, and KNN, allowing users to choose the most suitable algorithm for their forecasting needs. Time-series forecasting is crucial for making predictions about stock prices, which is essential for investment strategies. The project employs various machine learning regressors to predict the adjusted closing price of bitcoin stock. By training these models on historical data, users can obtain predictions for future adjusted closing prices. This information is invaluable for traders and investors looking to make buy or sell decisions. The project also incorporates hyperparameter tuning and cross-validation to enhance the accuracy of these predictions. These models employ metrics such as Mean Absolute Error (MAE), which quantifies the average absolute discrepancy between predicted values and actual values. Lower MAE values signify superior model performance. Additionally, Mean Squared Error (MSE) is used to calculate the average squared differences between predicted and actual values, with lower MSE values indicating better model performance. Root Mean Squared Error (RMSE), derived from MSE, provides insights in the same units as the target variable and is valued for its lower values, denoting superior performance. Lastly, R-squared (R2) evaluates the fraction of variance in the target variable that can be predicted from independent variables, with higher values signifying better model fit. An R2 of 1 implies a perfect model fit. In addition to close price forecasting, the project extends its capabilities to predict daily returns. By implementing grid search, users can fine-tune the hyperparameters of machine learning models such as Random Forests, Gradient Boosting, Support Vector, Decision Tree, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and AdaBoost Classifiers. This optimization process aims to maximize the predictive accuracy of daily returns. Accurate daily return predictions are essential for assessing risk and formulating effective trading strategies. Key metrics in these classifiers encompass Accuracy, which represents the ratio of correctly predicted instances to the total number of instances, Precision, which measures the proportion of true positive predictions among all positive predictions, and Recall (also known as Sensitivity or True Positive Rate), which assesses the proportion of true positive predictions among all actual positive instances. The F1-Score serves as the harmonic mean of Precision and Recall, offering a balanced evaluation, especially when considering the trade-off between false positives and false negatives. The ROC Curve illustrates the trade-off between Recall and False Positive Rate, while the Area Under the ROC Curve (AUC-ROC) summarizes this trade-off. The Confusion Matrix provides a comprehensive view of classifier performance by detailing true positives, true negatives, false positives, and false negatives, facilitating the computation of various metrics like accuracy, precision, and recall. The selection of these metrics hinges on the project's specific objectives and the characteristics of the dataset, ensuring alignment with the intended goals and the ramifications of false positives and false negatives, which hold particular significance in financial contexts where decisions can have profound consequences. Overall, the "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project serves as a powerful and user-friendly platform for financial data analysis and decision-making. It bridges the gap between complex machine learning techniques and accessible user interfaces, making financial analysis and prediction more accessible to a broader audience. With its comprehensive features, this project empowers users to gain insights from historical data, make informed investment decisions, and develop effective trading strategies in the dynamic world of finance. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/data-visualization-time-series.html.

Download or read book A Comparison of Machine Learning Algorithms in Predicting Nonnormal Continuous Outcome Variables written by Erin Crangle and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a type of data analysis that creates prediction models by learning from a portion of the data set. These algorithms can be used in many disciplines to answer complex questions and hypotheses. There are many available algorithms, each with their own strengths and weaknesses. Much research has been compiled on each algorithm individually to show where they excel and provide context into many use cases. The purpose of this research project is to document a comparison of BART, Random Forest, and GBM; A few top machine learning algorithms on their ability to predict nonnormal continuous outcome variables. The results of this study could help determine which prediction models preform the most efficiently and accurately when building predictive models for nonnormal continuous outcome variables.

Download or read book Efficient Continual Learning Framework for Stream Mining written by Zhuoyi Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent times, deep learning-based neural models have performed excellent intelligence in several real-world tasks (e.g. object recognition, speech recognition, and machine trans- lation). However, existing achievements are typically under a closed, static environment, compared with the human brain that can learn and perform in the changing, evolving dy- namic setting with new tasks, it is hard for the current intelligent agent that discovers the novel knowledge effectively, and incrementally learn such new skills fast and efficient. We could observe that the ability to learn and accumulate knowledge over the lifetime is an essential perspective of human intelligence. Under this scenario, how encouraging the agent continually discover and learn sequentially from non-stationary or online stream of data, is significant in real-world research and application. We consider a situation, that infinite stream of data sampled from a non-stationary distribu- tion with the sequence of new emerged tasks, the key factor of the continual learning process is to automatically discover the novel/unseen pattern in the new coming tasks (compared with previous data), and also reduce the knowledge forgetting of previously seen concepts. A common problem that current deep learning/machine learning models are well known to suffer from. The contribution we described in this dissertation could be expanded to mitigate the novel knowledge discovery, incrementally efficient learning of new skills, and reduce the forgetting phenomena in the deep learning algorithm. To approach such challenges in the continual learning scenario, we first describe a class- incremental learning setting where incoming task include new classes reaching to the agent at a time, and the previous tasks could not, or limited be accessed. We introduce specific background about existing technologies for solving different issues in the learning process, and then describe our developed frameworks that aim for high-level performance on each challenge. It reserves different specialist models for each goal, includes the discovery and further incremental learning of novel knowledge using a shared model with a limited, fixed capacity. Also, when accounting for privacy issues and memory constraints, we propose to update model parameters while only accessing the previous statistics information, instead of original data. As a result, the knowledge forgetting on old concepts is reduced, and storing original input could be avoided.

Download or read book Novel Methods for Mining and Learning from Data Streams written by Ammar Shaker and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we elaborate on knowledge acquisition and learning from non-stationary data streams. A data stream is formed by consecutively arriving data examples, whose data generating process may change in the course of time. Both the cumulative and the non-stationary nature of the data within a stream create a challenge for traditional machine learning methods.Concentrating on adaptive supervised learning from data streams, we introduce two novel learning methods: IBLStreams and eFPT. IBLStreams is an instance-based learner that shows how instance-based learning approaches, compared to model-based approaches, are naturally incremental besides their inherent ability to adapt upon the occurrence of a concept change. Evolving fuzzy pattern trees (eFPTs) utilize the potential interpretability of the fuzzy logic concepts in inducing compact trees; the induced trees offer the tradeoff between compact interpretable models and generalization performance. eFPTs attempt to dynamically evolve the induced tree in order to reflect any change in the underlying data generating process.We also introduce "recovery analysis" as a new type of evaluation for adaptive supervised learners on data streams. It is an experimental protocol to assess the learner's ability to learn and recover after a concept change. The resulting recovery pattern of the learning method can be analyzed both graphically and numerically using recovery measures.Apart from the full supervision offered in the streams studied in the previous approaches, we also consider streams of events: such a stream contains temporal events emitted from instances under observation. For a given instance, the survival time is the time this instance spends in the study until experiencing the event of interest. ... ; eng

Download or read book The Digital Patient written by Suchi Saria and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The current unprecedented rate of digitization of longitudinal health data -- continuous device monitoring data, laboratory measurements, medication orders, treatment reports, reports of physician assessments -- allows visibility into patient health at increasing levels of detail. A clearer lens into this data could help improve decision making both for individual physicians on the front lines of care, and for policy makers setting national direction. However, this type of data is high-dimensional (an infant with no prior clinical history can have more than 1000 different measurements in the ICU), highly unstructured (the measurements occur irregularly, and different numbers and types of measurements are taken for different patients) and heterogeneous (from ultrasound assessments to lab tests to continuous monitor data). Furthermore, the data is often sparse, systematically not present, and the underlying system is non-stationary. Extracting the full value of the existing data requires novel approaches. In this thesis, we develop novel methods to show how longitudinal health data contained in Electronic Health Records (EHRs) can be harnessed for making novel clinical discoveries. For this, one requires access to patient outcome data -- which patient has which complications. We present a method for automated extraction of patient outcomes from EHR data; our method shows how natural languages cues from the physicians notes can be combined with clinical events that occur during a patient's length of stay in the hospital to extract significantly higher quality annotations than previous state-of-the-art systems. We develop novel methods for exploratory analysis and structure discovery in bedside monitor data. This data forms the bulk of the data collected on any patient yet, it is not utilized in any substantive way post collection. We present methods to discover recurring shape and dynamic signatures in this data. While we primarily focus on clinical time series, our methods also generalize to other continuous-valued time series data. Our analysis of the bedside monitor data led us to a novel use of this data for risk prediction in infants. Using features automatically extracted from physiologic signals collected in the first 3 hours of life, we develop Physiscore, a tool that predicts infants at risk for major complications downstream. Physiscore is both fully automated and significantly more accurate than the current standard of care. It can be used for resource optimization within a NICU, managing infant transport to a higher level of care and parental counseling. Overall, this thesis illustrates how the use of machine learning for analyzing these large scale digital patient data repositories can yield new clinical discoveries and potentially useful tools for improving patient care.